This study aims to understand the South African political context by
analysing the sentiments shared on Twitter during the local government
elections. An emphasis on the analysis was placed on understanding the
discussions led around four predominant political parties ANC, DA, EFF and
ActionSA. A semi-supervised approach by means of a graph-based technique to
label the vast accessible Twitter data for the classification of tweets into
negative and positive sentiment was used. The tweets expressing negative
sentiment were further analysed through latent topic extraction to uncover
hidden topics of concern associated with each of the political parties. Our
findings demonstrated that the general sentiment across South African Twitter
users is negative towards all four predominant parties with the worst negative
sentiment among users projected towards the current ruling party, ANC, relating
to concerns cantered around corruption, incompetence and loadshedding.

本研究通过使用基于图形的半监督学习方法，使用情感分析和潜在主题提取技术来分析南非选举期间 Twitter 上的情感和主题，结果表明南非 Twitter 用户普遍对 ANC、DA、EFF 和 ActionSA 这四个主要政党持负面态度，对现任执政党 ANC 表现出最严重的负面情感，与腐败、无能和负荷调节问题有关。

预测南非地方选举政治情绪的半监督学习方法

Semi-supervised learning approaches for predicting South African political sentiment for local government elections

Language models are the foundation of current neural network-based models for
natural language understanding and generation. However, research on the
intrinsic performance of language models on African languages has been
extremely limited, which is made more challenging by the lack of large or
standardised training and evaluation sets that exist for English and other
high-resource languages. In this paper, we evaluate the performance of
open-vocabulary language models on low-resource South African languages, using
byte-pair encoding to handle the rich morphology of these languages. We
evaluate different variants of n-gram models, feedforward neural networks,
recurrent neural networks (RNNs), and Transformers on small-scale datasets.
Overall, well-regularized RNNs give the best performance across two isiZulu and
one Sepedi datasets. Multilingual training further improves performance on
these datasets. We hope that this research will open new avenues for research
into multilingual and low-resource language modelling for African languages.

本文通过研究对南非低资源语言的开放词汇语言模型的表现来评估不同变种的 N 元模型、前馈神经网络、循环神经网络和 Transformers 网络。这项研究有望为非洲语言的多语种和低资源语言建模开辟新的研究途径。

南非语言低资源语言建模

Low-Resource Language Modelling of South African Languages

Recent advances in neural machine translation (NMT) have led to
state-of-the-art results for many European-based translation tasks. However,
despite these advances, there is has been little focus in applying these
methods to African languages. In this paper, we seek to address this gap by
creating an NMT benchmark BLEU score between English and the ten remaining
official languages in South Africa.

该研究旨在解决针对非洲语言的神经机器翻译方法应用的空缺问题，通过在英语和南非其余十种官方语言之间创建 NMT 基准 BLEU 得分来进行研究。